The prevalence of ENA nonusers was higher among males than females (PR 0.93, 95% CI 0.89‐0.97) and in the age groups above 34 years. There were no significant regional differences between ENA users and nonusers (Table 1).

ENA users had a higher level of education more often than nonusers. Compared to those that had primary or national school-level education, ENA use was more prevalent among those that finished high school (PR 1.22, 95% CI 1.12‐1.34), a university of applied sciences (PR 1.25, 95% CI 1.14‐1.37), college (PR 1.26, 95% CI 1.14‐1.38), or university or college (PR 1.33, 95% CI 1.22‐1.45; Table 1).

Compared to employees, ENA use was more prevalent among those holding functionary, higher functionary, and leadership positions as well as among students. Nonusers were more often unemployed (PR 0.75, 95% CI 0.67‐0.84; Table 1).

ENA users were more often couples (PR 1.10, 95% CI 1.04‐1.16) or living in households with children (PR 1.15, 95% CI 1.09‐1.21; Table 1).

The most frequent reasons for not activating the ENA after download were perceived lack of usefulness of the ENA for pandemic control (35%) or for oneself (34%). Around 24% of respondents indicated phone battery life and staying at home as reasons for nonactivation (Figure 3).

ENA users reported at least part-time adherence to general pandemic guidance more often than nonusers, ranging from avoiding errands (PR 1.24, 95% CI 1.19‐1.29) to wearing a mask (PR 1.65, 95% CI 1.45‐1.91; Table 2). However, these associations may reflect self-selection and cannot be interpreted as causal.

Based on survey data, of those ENA users reporting a positive SARS-CoV-2 result (n=881, weighted n=904), close to 70% did not receive a token and 1% reported being diagnosed abroad. Of the 30% receiving a token (n=259, weighted n=269), most (79%) reported entering it into the ENA. For approximately 72% of users, tokens were delivered in under 48 hours from diagnosis, and of those, 51% were received within 24 hours. However, delay in token delivery by 3‐4 days was reported by 17%, 5‐7 days by 3%, and over 7 days by 2%. Six percent did not recall the speed of token delivery.

These survey-based token-use estimates are not directly comparable with token use proportions calculated based on ENA backend data, as the backend metrics reflect all eligible users over the full study period, whereas survey metrics reflect self-reported experiences in a cross-sectional respondent sample.

Influence of ENA on user behavior was assessed based on data from the Koronavilkku survey.

The majority of ENA users who received a notification of potential exposure adhered at least part-time to all guidance provided in the ENA. The least adhered to recommendations were “to avoid running errands” (34% reported no adherence), “work from home” (25% no adherence), and “avoid social interactions” (16% no adherence; Figure 4).

Looking at ENA users (n=2740, weighted n=2927), Bowker’s test of symmetry showed significant differences between general adherence to pandemic recommendations versus after receiving an exposure notification for working from home (χ²₃=75.5; P<.001), self-observation of well-being (χ²₃=18.3; P<.001), avoiding social interactions (χ²₃=114.6; P<.001), wearing a mask (χ²₃=26.9; P<.001), and avoiding errands (χ²₃=74.4; P<.001).

Adherence to hand hygiene (χ²₃=0.6; P=.90) and cough hygiene (χ²₃=0.6; P=.90) guidelines showed no differences.

Further analysis of transition patterns in adherence to pandemic recommendations suggested the following:

These transition ORs are descriptive and should not be interpreted as inferential tests of specific pairwise changes.

A negative test was the most stated reason for not adhering, partly adhering, or discontinuing adherence to exposure notification guidance (23%). Other commonly reported reasons included receiving instructions from health care (21%), being asymptomatic (21%), receiving instructions from employer (20%), or being put in mandatory quarantine as defined by the Communicable Diseases Act (20%; Figure 5).

Among ENA users receiving an exposure notification (n=644, weighted n=670), more than 50% contacted health care afterwards either by phone (25%) or through the Finnish electronic service platform providing symptom checks and guidance called “Omaolo” (DigiFinland Oy; 27%). Around 45% did not report contacting health care and the remaining 3% were unsure.

The ENA’s acceptability was assessed based on data from the Koronavilkku survey.

Among 4061 respondents (weighted n=4334), 33% never downloaded or activated the ENA. Another 34% downloaded and actively used it at the time of the survey, while 33% downloaded the ENA but later removed it or did not activate it again on a new mobile phone.

Among former ENA users, the most common reasons for discontinuation of ENA use included not seeing any use for pandemic control (34%) or to oneself (32%). Negative effect on battery life was the third most common reason (26%; Figure 6).

Opinions on the quality of the ENA were divided. Almost half (49%) of the respondents at least partly agreed with the statement “Koronavilkku was a good application.” Almost two-thirds of respondents (62%) at least partly agreed that they would use the ENA if it was relaunched (Figure 7A).

Among ENA users, this percentage was higher: 63% at least partly agreed that it was a good ENA, and 78% stated that they would use it again (Figure 7B).

Between 62%‐78% of respondents stated they would have accepted more data (such as additional information about exposure or localization data) being collected by the ENA or being sent to notify health authorities and for situation monitoring or statistical analyses (Figure 7A). The percentage of respondents who at least partly agreed with all statements was higher among ENA users (Figure 7B).

Sentiment analysis using an LLM was performed for open-ended feedback provided by 813 respondents, 557 (69%) of whom were ENA users.

In comparison to the survey respondents, open-ended feedback was provided more often by males, aged 15‐24 years, and persons living in Helsinki-Uusimaa and Western Finland regions (Table 3).

Sentiment analysis determining the emotional tone of the open-ended feedback shows that most provided feedback had a negative sentiment (316/813, 39%), while positive-oriented feedback was provided by 19% (151/813) of respondents. Overall, 12% (100/813) of the provided feedback was not understandable by the model.

A higher percentage of positive responses were provided by ENA users than non-ENA users (24%, 134/557 vs 6.6%, 17/256). Nonuser comments more often expressed negative or neutral sentiments (48%, 122/256 and 32%, 83/256, respectively) than ENA user comments (35%, 194/557, and 29%, 163/557, respectively; Figure 8).

The Koronavilkku ENA saw widespread adoption during its initial months, with peak active coverage reaching approximately 53% of smartphone users and 45% of the general population. However, as the pandemic progressed, token use declined, especially among people diagnosed with COVID-19, a key target group, with fewer than half of notified COVID-19 cases registered in the ENA, and this proportion fell over time. This trend may reflect reduced app use, reduced token issuance, or a combination of both. Despite this, overall user engagement remained relatively high throughout the ENA’s operational period.

ENA effectiveness is closely tied to its level of adoption. Studies indicate that even modest uptake (15%‐30%) can reduce infections [5,6], while others provide more conservative estimates, indicating that 70%‐80% coverage among smartphone users (56% in the general population) is required to control the pandemic effectively [7,8]. Achieving 80% ENA coverage has been linked to a reduction in COVID-19 mortality by up to 85% [9]. Despite these varying results, one could extrapolate that ENA effectiveness is complex and influenced by multiple factors, with partial benefits seen already at lower coverage rates of around 20% [10]. Notably, Koronavilkku achieved the highest active ENA user coverage among European Union and European Economic Area countries [19].

Our findings identified demographic disparities in ENA use. Males, individuals 35 or older, and those living alone were less likely to use the ENA, while females, those with higher education, and households with children were more likely to be ENA users. However, studies from other European countries revealed differing and sometimes contradictory results. A French study found no significant link between sociodemographic factors and ENA use, except for financial deprivation [12], while a German study reported that males and those 65 years and older were more likely to use the national ENA [13]. Results from the latter study associating higher education levels with increased ENA use were in line with our findings [13]. On the other hand, a different German study indicated that those who were more unwilling to install the national ENA and use it correctly were men and those aged between 30 and 59 years old [14]. These differences suggest that country-specific cultural and contextual differences also play a significant role in ENA use.

A major barrier to the ENA’s performance was token issuance. Over two-thirds of COVID-19-positive users did not receive a token in connection with contact tracing by health care professionals. However, among those who did, most entered their token into the ENA, suggesting strong user motivation when the system functioned properly. Initially, tokens were issued manually by health care contact tracing teams, but high workloads likely caused delays. Recognizing this, Finland implemented partial automation of token issuance in some regions between June and October 2021 [4]. Despite this, issuing exposure tokens remained a significant challenge, particularly during high-incidence periods such as the Omicron wave in late 2021-early 2022. Wider automation of token issuance based on positive results from local systems or a national patient data repository instead of providing a separate system to health care providers would have likely increased the number of issued tokens and shortened token delivery times. However, these possibilities were constrained by a lack of readily available system integrations, risks of not getting complete and reliable result data from the national repository, and short validity periods of tokens. The process for automated token issuance has been described in detail elsewhere [4].

Timely exposure notifications are crucial for effective mitigation. Ferretti et al [20] found that reducing quarantine delays from three to two days significantly curtailed SARS-CoV-2 spread, provided a large portion of the population used the national ENA. Similarly, a Swiss study showed that ENA users quarantined earlier than those not warned by the ENA, though delays in token issuance, linked to manual contact tracing bottlenecks, remained a challenge [16]. A study from Singapore demonstrated that automating public health responses expedited the issuance of quarantine orders compared to manual contact tracing [17].

Our study also explored reasons for nonactivation of the ENA. Consistent with findings from other studies, the most commonly cited reason for not using or uninstalling the ENA was a perceived lack of usefulness [15,21]. Additionally, concerns about battery drain were mentioned as a reason for removing the ENA [21]. These barriers emphasize the need for improved communication around the ENA’s intended benefits and for technical solutions to mitigate the effect on battery life.

Most users who received exposure notifications followed at least some pandemic guidelines recommended by the ENA. ENA users reported adherence at least part-time to general pandemic guidance more often than nonusers, which may reflect self-selection, with ENA users being more health-conscious in general compared to those who did not download the ENA.

Notifications were associated with higher reported adherence among partially compliant users but showed little association among noncompliant individuals. A UK study similarly found high self-reported compliance with self-isolation recommendations following exposure notifications [22].

The only recommendation with declining adherence was “working from home.” This likely reflects job constraints (remote work was not feasible for all occupations) or legal arrangements under which official isolation or quarantine was treated as sick leave, with no obligation to work remotely, even though it was highly recommended by the government [23,24]. Over half of notified users sought health care guidance, which may indicate the ENA played a role in prompting further preventive actions. However, about 20% cited a negative COVID-19 test result as their reason for not fully adhering to guidelines.

The ENA’s general acceptability was relatively high, with 68% of respondents using it at least once. However, nearly half eventually stopped using it, particularly as the pandemic prolonged. Similarly, users from other countries also reported their ENAs as trustworthy, reliable, and user-friendly [25]. Nevertheless, a study in Belgium found that features focused on informing citizens or facilitating test appointments were viewed more favorably than those related to access and control [15]. In addition, ENA acceptability has been linked to trust in government, scientists, and health care professionals [12].

A substantial proportion of users expressed interest in enhanced features such as automated exposure notifications to health care providers and improved data tracking. However, it is crucial to balance these functionalities with user privacy concerns that can also evolve over time. In the development of the Finnish ENA, addressing such concerns was a priority. At no point did the ENA process or have access to the identities of users or of persons they encountered, irrespective of infection status. Furthermore, location data from encounters were neither collected nor used, despite the technical feasibility of doing so. Notably, a German study found that public acceptance of privacy-invading technologies, such as tracking, was higher at the beginning of the pandemic, but this acceptance waned as the pandemic continued [26].

Our sentiment analysis of open-ended feedback suggests that users generally expressed more positive attitudes toward the ENA compared to nonusers. While a considerable portion of the feedback reflected neutral or negative sentiments, this should be interpreted with caution. Research on response bias suggests that people are more likely to provide open-ended feedback when they have strong opinions or concerns (either very positive or very negative), whereas users with more moderate views may be less inclined to respond without specific prompting [27-29]. As a result, the overall sentiment of the user base may be more positive than the open-ended responses alone suggest. Additionally, there appear to be differences between those who chose to provide open feedback and the broader survey respondents, suggesting that certain demographics were more inclined to share their views. In contrast, a study conducted in Ireland found that the overall perception of their COVID-19 ENA was mostly positive (90%), although they conducted their sentiment analysis manually [30].